{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROBER = \"TCPIP0::192.168.0.2::800::SOCKET\"\n",
    "SMU = \"GPIB0::17::INSTR\"\n",
    "SWITCH = \"GPIB0::22::INSTR\"\n",
    "\n",
    "import pyvisa\n",
    "rm = pyvisa.ResourceManager()\n",
    "\n",
    "prober = rm.open_resource(PROBER)\n",
    "prober.read_termination = '\\r\\n'\n",
    "B1500a = rm.open_resource(SMU)\n",
    "B2201 = rm.open_resource(SWITCH)\n",
    "rm.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prober stage\n",
    "prober.query(\"*IDN?\")\n",
    "prober.write(\"move_chuck_contact\")\n",
    "prober.write(\"move_chuck_separation\")\n",
    "#prober.write(\"map:step_first_die\")\n",
    "prober.write(\"map:step_next_die\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROBER = \"TCPIP0::192.168.0.2::800::SOCKET\"\n",
    "\n",
    "\n",
    "import pyvisa\n",
    "rm = pyvisa.ResourceManager()\n",
    "\n",
    "prober = rm.open_resource(PROBER)\n",
    "prober.read_termination = '\\r\\n'\n",
    "\n",
    "prober.write(\"map:step_next_die\")\n",
    "\n",
    "prober.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 开关矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def matrixx(pos):\n",
    "    SWITCH = \"GPIB0::22::INSTR\"\n",
    "\n",
    "    import pyvisa\n",
    "    rm = pyvisa.ResourceManager()\n",
    "    B2201 = rm.open_resource(SWITCH)\n",
    "\n",
    "    #switching matrix\n",
    "    import B2200\n",
    "    import time\n",
    "    #1,1234-1234\n",
    "    #2,1234-5678\n",
    "    #3,1234-9-12\n",
    "    #4,openall\n",
    "    B2200.Load_Path(B2201,pos)\n",
    "    #time.sleep(2)\n",
    "    #B2200.Load_Path(B2201,2)\n",
    "    #time.sleep(2)\n",
    "    #B2200.Load_Path(B2201,3)\n",
    "    #time.sleep(2)\n",
    "    #B2200.Load_Path(B2201,4)\n",
    "\n",
    "    rm.close()\n",
    "matrixx(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "B1500a.query(\"*IDN?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 单个器件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NMOS-DIE-0-20240904_142644-0.1VV"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "#b1500a smu\n",
    "from starInstrument import *\n",
    "#pos=2\n",
    "#matrixx(pos)\n",
    "\n",
    "def timemap(tstr):\n",
    "    l = tstr.split(',')\n",
    "    if l[1]=='1':\n",
    "        return str(0.08*int(l[2]))+'ms'\n",
    "    else:\n",
    "        return str(20*int(l[2]))+'ms'\n",
    "from matplotlib import pyplot as plt\n",
    "n=1\n",
    "from itertools import product\n",
    "\n",
    "for tp,i,DrainI in product(\n",
    "    ['P','N'],range(n),[0.1]\n",
    "    ):\n",
    "        #matrixx(pos)\n",
    "        #True False\n",
    "        if tp=='P':\n",
    "            Vds = 0-DrainI\n",
    "            df = transCurve(tp,ADC='AIT 1,2,1',\n",
    "                useDefault=False,startV=-2,endV=0.5,Vds=0-DrainI,\n",
    "                chnSource='1')\n",
    "        else:\n",
    "            Vds = DrainI\n",
    "            df = transCurve(tp,ADC='AIT 1,2,1',\n",
    "                useDefault=False,startV=2,endV=-0.5,Vds=DrainI,\n",
    "                chnSource='4')\n",
    "        plt.plot(df['GateV'],abs(df['SourceI']),label=f'{tp}MOS:{Vds}V')\n",
    "        #plt.plot(df['GateV'],abs(df['DrainI']),label=f'{tp}DrainI')\n",
    "        plt.yscale('log')\n",
    "        from datetime import datetime\n",
    "        tStamp = datetime.strftime(datetime.now(),\"%Y%m%d_%H%M%S\")\n",
    "        df.to_excel(f\"./result/{tp}MOS-1500SP-{tStamp}-{Vds}V.xlsx\",sheet_name='Data')\n",
    "        print(f\"\\r{tp}MOS-DIE-{i}-{tStamp}-{Vds}V\",end='')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "closeIns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NMOS-DIE-20240925_093658-1/1"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from starInstrument import *\n",
    "from matplotlib import pyplot as plt \n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "pos = 2\n",
    "matrixx(pos)\n",
    "\n",
    "from itertools import product\n",
    "n=1#times\n",
    "sleepTime = 0\n",
    "for i,tp in product(\n",
    "    range(n),\n",
    "    ['P','N']\n",
    "    ):\n",
    "    #True False\n",
    "    df = transCurve(DeviceType=tp,ADC='AIT 1,2,1',t_hold=1)\n",
    "    plt.plot(df['GateV'],abs(df['SourceI']),label=f'{tp}SourceI')\n",
    "    plt.yscale('log')\n",
    "    from datetime import datetime\n",
    "    tStamp = datetime.strftime(datetime.now(),\"%Y%m%d_%H%M%S\")\n",
    "    df.to_excel(f\"./result/{tp}MOS-{tStamp}-{i+1}.xlsx\",sheet_name='Data')\n",
    "    print(f\"\\r{tp}MOS-DIE-{tStamp}-{i+1}/{n}\",end='')    \n",
    "    #if tp == 'N' and i<n:\n",
    "    #    time.sleep(sleepTime)#unit second\n",
    " \n",
    "#plt.legend()\n",
    "plt.show()\n",
    "closeIns()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#b1500a smu\n",
    "def inverter():\n",
    "    PROBER = \"TCPIP0::192.168.0.2::800::SOCKET\"\n",
    "    SMU = \"GPIB0::17::INSTR\"\n",
    "    SWITCH = \"GPIB0::22::INSTR\"\n",
    "\n",
    "    import pyvisa\n",
    "    rm = pyvisa.ResourceManager()\n",
    "\n",
    "    B1500a = rm.open_resource(SMU)\n",
    "    B1500a.write('*RST')\n",
    "    B1500a.write('CN')\n",
    "\n",
    "    #--------------------IV_Sweep函数测试参数示例（双变量扫描）------------------#\n",
    "    Unit1='3'                     #扫描变量1对应的物理端口为4\n",
    "    VName1='VVin'                   #扫描变量1的名字为VD，注意电压模式的端口命名为V....,电流模式端口命名为I...\n",
    "    Direction1='Single Linear'    #扫描变量1的方向为Double，并且加电压模式为Linear（更多模式见34）\n",
    "    Start1=0                   #扫描变量1的起始电压为-3 V\n",
    "    end1=2.0                      #扫描变量1的终止电压为3 V\n",
    "    nop1=101                      #扫描变量1的点数目为201（Double的话，实际数据点为201*2）\n",
    "    Comp1=1e-2                  #扫描变量1的限流值\n",
    "    range1='Auto'         #扫描变量1的测量Range\n",
    "\n",
    "    VAR_setup=[Unit1,VName1,Direction1,Start1,end1,nop1,Comp1,range1] #打包扫描变量，如果单变量扫描，不用第二行\n",
    "\n",
    "    Measurement_setup = []\n",
    "    Unit='1'                    #测量变量1的物理端口为\n",
    "    VName='VVdd'                  #测量变量1的名称，注意测电流是固定电压名称为V...，测电压是固定电流，名称是I...\n",
    "    VorI=2.0                 #测量变量1的固定电压为0 V\n",
    "    Comp=1e-2                  #测量变量1的限流值\n",
    "    range='Auto'                #测量变量1的Range\n",
    "    Measurement_setup  += [Unit,VName,VorI,Comp,range] #打包测量变量，有几个打包几个\n",
    "\n",
    "    Unit='4'                    #测量变量1的物理端口为\n",
    "    VName='VGND'                  #测量变量1的名称，注意测电流是固定电压名称为V...，测电压是固定电流，名称是I...\n",
    "    VorI=0.0                 #测量变量1的固定电压为0 V\n",
    "    Comp=1e-2                  #测量变量1的限流值\n",
    "    range='Auto'                #测量变量1的Range\n",
    "    Measurement_setup  += [Unit,VName,VorI,Comp,range] #打包测量变量，有几个打包几个\n",
    "\n",
    "    Unit='2'                    #测量变量1的物理端口为\n",
    "    VName='IVout'                  #测量变量1的名称，注意测电流是固定电压名称为V...，测电压是固定电流，名称是I...\n",
    "    VorI=0.0                 #测量变量1的固定电压为0 V\n",
    "    Comp=5                  #测量变量1的限流值\n",
    "    range='Auto'                #测量变量1的Range\n",
    "    Measurement_setup  += [Unit,VName,VorI,Comp,range] #打包测量变量，有几个打包几个\n",
    "    #print(Measurement_setup)\n",
    "    B1500a.timeout = 60000\n",
    "    B1500a.write('*RST')\n",
    "    B1500a.write('CN 1,2,3,4')\n",
    "    import B1500\n",
    "    res = B1500.IV_sweept(B1500a,VAR_setup,Measurement_setup)\n",
    "\n",
    "    import pandas\n",
    "    df = pandas.DataFrame(res[1:],columns=['Vin','Iin','Vdd','Idd','VGND','IGND',\"Iout\",'Vout'],dtype=float)\n",
    "    B1500a.close()\n",
    "    return df\n",
    "df = inverter()\n",
    "from matplotlib import pyplot as plt \n",
    "plt.plot(df['Vin'],df['Vout'])\n",
    "plt.show()\n",
    "\n",
    "from datetime import datetime\n",
    "tStamp = datetime.strftime(datetime.now(),\"%Y%m%d_%H%M%S\")\n",
    "df.to_excel(f\"./result/inverter_{tStamp}.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     GateV         GateI  DrainV    DrainI  SourceV       SourceI\n",
      "0   -0.500 -7.580000e-10     2.0  0.000102      0.0  2.000000e-11\n",
      "1   -0.475 -6.130000e-10     2.0  0.000102      0.0  2.100000e-11\n",
      "2   -0.450 -7.160000e-10     2.0  0.000102      0.0 -8.300000e-11\n",
      "3   -0.425 -6.970000e-10     2.0  0.000102      0.0  2.500000e-11\n",
      "4   -0.400 -6.870000e-10     2.0  0.000102      0.0 -4.600000e-11\n",
      "..     ...           ...     ...       ...      ...           ...\n",
      "96   1.900  1.300000e-10     2.0  0.033238      0.0 -3.317800e-02\n",
      "97   1.925  2.210000e-10     2.0  0.035030      0.0 -3.496000e-02\n",
      "98   1.950  1.730000e-10     2.0  0.036751      0.0 -3.664500e-02\n",
      "99   1.975  2.530000e-10     2.0  0.038498      0.0 -3.842800e-02\n",
      "100  2.000  2.670000e-10     2.0  0.040341      0.0 -4.032100e-02\n",
      "\n",
      "[101 rows x 6 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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244Bioxx67qYsxcVEWh0JFmtxeVm1apXGjx/fdD8vL0+SNG3aNM2ZM0fXXXed9u3bp1mzZqm0tFSZmZlatGjRcSfx+pvT6ZTT6ZTX623T9wEAtJ9Pt5brj59ulyQ9ftUIDUyOszgRAsEZjfMSiFpynTgAIHCVump12f99porqOt10Ti/9espwqyOhDVk2zgsAAP7Q4PXpP95co4rqOqV3j9cvLk+3OhICCOUFABBw/nfxNq0oqlDHKIecN45iIDocg/ICAAgon23bp2c+abww5LGrRqhvUkeLEyHQUF4AAAFjX6VH/zm3QMZIN+T20g8yUq2OhAAUMuXF6XQqPT1d2dnZVkcBALSCMUYPvbNW+6vqNCQlTrO+z3kuODGuNgIABISXl32jWQs3KirCrnfvOU+DU7gsOpxwtREAIKhsK6vUb97bLEma+b0hFBecEuUFAGApT4NX971ZIE+DTxcM6qpbx/axOhICHOUFAGCpP3z4tTbtdSuxY5SevJp5i3B6lBcAgGW+3L5fz3+2Q5L02yuHq1t8jMWJEAwoLwAAS1R5GvTg2+tkjDQ1J00Th6ZYHQlBgvICALDE//xzs3YfOqyeZ3Vg+H+0SMiUF8Z5AYDg8dm2fXp9+U5J0hNXZ6hjdITFiRBMGOcFANCuKmvrNemppdrjqtW0Mb31yBXDrI6EAMA4LwCAgPWb9zZrj6tWvRJj9fPvDbE6DoIQ5QUA0G4+3VquN1eWSJKeuHqEYqM4XISWo7wAANpFZW29Zs5bL0m67dw+yu3XxeJECFaUFwBAu/jdoq3a66pV7y6xemgSh4vQepQXAECbyy+u0KvLiyVJj/1wuDpEOSxOhGBGeQEAtKm6Bp9mzlsvY6Rrsnpq7IAkqyMhyFFeAABt6tkl2/V1WZW6dIzSf11+ttVxEAJCprwwSB0ABJ7t+6r0zMeFkqRZk9PVOTbK4kQIBQxSBwBoEz6f0fUvfKUVRRUaN7irXro1mxmjcVIMUgcAsNxbq0q0oqhCHSIdevSKYRQX+A3lBQDgdxXVdfrtoi2SpAcmDlJaYqzFiRBKKC8AAL/73aItOlRTryEpcbp1bB+r4yDEUF4AAH61eufBpikAfj1lmCIcbGrgX3yjAAB+4/UZ/feCDZKkq7N6anSfRIsTIRRRXgAAfvPqV8XauMet+JgIzWDGaLQRygsAwC/2VXr05IdbJUkPXjpESZ2iLU6EUEV5AQD4xWP/3KzK2gYN75GgG3J6WR0HISxkygsj7AKAdfKLKzRvzW7ZbNKjU4bJYWdMF7QdRtgFAJwRn8/oh3/6UmtLDuna0T31u6szrI6EIMQIuwCAdvPuuj1aW3JIsVEO/WziYKvjIAxQXgAArXa4zqvH328cSffucf3VLT7G4kQIB5QXAECrvfjZDu1x1apH5w760fn9rI6DMEF5AQC0Srm7Vn9asl2S9NClgxUT6bA4EcIF5QUA0CpPfrhVNXVejezVWT/ISLU6DsII5QUA0GIbdrv0dv4uSdIvLk+Xzcal0Wg/lBcAQIv99v0tMkaanJGqrN5nWR0HYYbyAgBokc+27dPnhfsV5bDroUlcGo32R3kBADSbz2f02yOXRt90Tm+lJcZanAjhiPICAGi2f6zfq4173OoUHaF7LhpgdRyEqZApL8xtBABtq67Bpyc/aJw1+scX9FNixyiLEyFchUx5mT59ujZt2qSVK1daHQUAQtIbK3ZqZ0WNkjpF647z+1odB2EsZMoLAKDtVHka9PTH2yRJ900YqNioCIsTIZxRXgAAp/XiZzu0v6pOfbrE6vrsNKvjIMxRXgAAp3SgyqMXlu6QJD04aYgiHWw6YC2+gQCAU3p2yXZV13k1vEeCLhueYnUcgPICADi5cnetXl5WLEnKmziIaQAQECgvAICT+uOn2+Vp8GlUr84aN6ir1XEASZQXAMBJ7Dl0WK8v3ylJemDiYPa6IGBQXgAAJ/TMJ4Wq8/qU2zdRY/t3sToO0ITyAgA4TklFjd5aWSKJvS4IPJQXAMBx/m/xNjX4jM4fmKScvolWxwGOQXkBAByjaH+15q3ZLUnKu2SQxWmA41FeAADHeHrxNnl9RhcN6aaRvc6yOg5wHMoLAKBJ8YFqLVy7R5J0/4SBFqcBTozyAgBo8uyS7fL6jMYN7qoRPTtbHQc4IcoLAECStPvQYb2Tv0uSdO9FAyxOA5xcyJQXp9Op9PR0ZWdnWx0FAILS80u2q95rNKZfF2X15gojBC6bMcZYHcKf3G63EhIS5HK5FB8fb3UcAAgK5ZW1Ou/xT1TX4NPrP8rV2AFJVkdCmGnJ9jtk9rwAAFrvxc+KVHdkDqMxjKaLAEd5AYAwV1Fdp1e/apw5+t6LBzKaLgIe5QUAwtxfPi9STZ1Xw3rEM3M0ggLlBQDCmLu2Xn/98htJ0j3j2euC4EB5AYAw9vrynar0NGhgt06amJ5sdRygWSgvABCmPA1e/eXzIknSjy/sL7udvS4IDpQXAAhTC9fsUXmlRynxMfpBRqrVcYBmo7wAQBjy+Yye/2yHJOn28/ooKoLNAYIH31YACEMfbylXYXmV4qIjNDWnl9VxgBahvABAGHp+aeNelxvO6aW4mEiL0wAtQ3kBgDCTX3xQK76pUKTDptvP7Wt1HKDFKC8AEGaeX7pdkjQls4eS42MsTgO0HOUFAMLIjn1V+nBTmSTprgv6WZwGaB3KCwCEkRc/L5Ix0sVDumlgcpzVcYBWobwAQJg4WF2neat3SZLuZK8LghjlBQDCxOsrdqq23qehqfHK7ZtodRyg1SgvABAG6r0+vbzsG0nS7ef2ZQJGBDXKCwCEgX+u36syt0dJnaL1/YzuVscBzgjlBQBCnDGmaQLGW8b0VnSEw+JEwJmhvABAiFu986DW7nIpKsKuG3KZCgDBL2TKi9PpVHp6urKzs62OAgAB5S+ffyNJmpKZqqRO0daGAfzAZowxVofwJ7fbrYSEBLlcLsXHx1sdBwAstetgjS743SfyGen9+87X2d35u4jA1JLtd8jseQEAHO/lZcXyGWls/y4UF4QMygsAhKhqT4PeWLFTknTHeUzAiNBBeQGAEDV/zW5V1jaoT5dYjR/czeo4gN9QXgAgBBlj9MqyYknSTef0lt3OoHQIHZQXAAhBK4oqtLWsUjGRdl2TlWZ1HMCvKC8AEIJe/qpxr8uUzB5KiI20OA3gX5QXAAgx5e5afbChVJJ085jeFqcB/I/yAgAh5vUVO9XgM8rqfZaGpiZYHQfwO8oLAISQeq9Pry9vvDz6Fva6IERRXgAghHy4sUzllR4ldYrSpcNSrI4DtAnKCwCEkJeXfSNJuj67F7NHI2RRXgAgRGwtrdTyogrZbWL2aIQ0ygsAhIhXj1wePTE9RamdO1icBmg7lBcACAE1dQ2av2a3JC6PRuijvABACHh37R5VeRrnMRrTr4vVcYA2RXkBgBBw9PLo63N6MY8RQh7lBQCC3IbdLq3d5VKkw6ars3paHQdoc5QXAAhyb6xo3OsycWiKkjpFW5wGaHuUFwAIYtWeBi0s2CNJujGHy6MRHigvABDEjjlRtz8n6iI8UF4AIIgdPWQ0NaeXbDZO1EV4oLwAQJDiRF2EK8oLAASpo3tdJg1NURdO1EUYobwAQBD69om6zGOEcEN5AYAg9N66varyNKhvUkdG1EXYobwAQBCau6pEknTt6DRO1EXYobwAQJApLK9UfvFBOew2XTWqh9VxgHZHeQGAIPPWql2SpPGDu6pbfIzFaYD2R3kBgCBS7/Vp3urG8nLt6DSL0wDWoLwAQBBZvLlc+6vqlNQpWuOHdLM6DmAJygsABJG3jpyoe1VWD0U6+BOO8BRw3/xDhw5p9OjRyszM1LBhw/TCCy9YHQkAAkKpq1afbi2XxCEjhLcIqwN8V1xcnJYuXarY2FhVV1dr2LBhuvLKK9WlC+MYAAhvf1u9Sz4jZfc5S/27drI6DmCZgNvz4nA4FBsbK0nyeDwyxsgYY3EqALCWz2eaDhmx1wXhrsXlZenSpZo8ebJSU1Nls9m0YMGC45ZxOp3q06ePYmJilJubqxUrVrToPQ4dOqSMjAz17NlTDz74oJKSkloaEwBCyvKiChUfqFHHKIcuH9Hd6jiApVpcXqqrq5WRkSGn03nCn8+dO1d5eXl6+OGHtXr1amVkZGjSpEkqLy9vWubo+Szfve3Z0zhPR+fOnbV27VoVFRXp9ddfV1lZWSs/HgCEhqN7XSZnpCo2KuCO+APtymbO4JiMzWbT/PnzNWXKlKbHcnNzlZ2drWeeeUaS5PP5lJaWpnvvvVczZsxo8Xvcfffduuiii3T11Vef8Ocej0cej6fpvtvtVlpamlwul+Lj41v8fgAQaCpr65X9m3+ptt6nv/10rLJ6n2V1JMDv3G63EhISmrX99us5L3V1dcrPz9eECRP+/QZ2uyZMmKBly5Y16zXKyspUWVkpSXK5XFq6dKkGDx580uUfe+wxJSQkNN3S0jgWDCC0vLdur2rrferXtaNG9epsdRzAcn4tL/v375fX61VycvIxjycnJ6u0tLRZr1FcXKzzzz9fGRkZOv/883Xvvfdq+PDhJ11+5syZcrlcTbeSkpIz+gwAEGjeyW8cUfeaLCZhBKQAvFQ6JydHBQUFzV4+Ojpa0dHRbRcIACy0Y1+VVhUflN0mXckkjIAkP+95SUpKksPhOO4E27KyMqWkpPjzrQAgLPztyDxGFwzqqmQmYQQk+bm8REVFKSsrS4sXL256zOfzafHixRozZow/3woAQp7XZ/S3/N2SGg8ZAWjU4sNGVVVVKiwsbLpfVFSkgoICJSYmqlevXsrLy9O0adM0evRo5eTkaPbs2aqurtZtt93m1+AAEOq+KNyvUnetEjpE6uKzmYQROKrF5WXVqlUaP3580/28vDxJ0rRp0zRnzhxdd9112rdvn2bNmqXS0lJlZmZq0aJFx53E629Op1NOp1Ner7dN3wcA2svbR07UvSIzVTGRDovTAIHjjMZ5CUQtuU4cAAKV63Dj2C51DT79/Z5zNaJnZ6sjAW3KsnFeAAD+8e7aPapr8GlwcpyG90iwOg4QUCgvABCAjo7tcnVWT8Z2Ab6D8gIAAaawvEoFJYfksNs0ZSRjuwDfRXkBgAAz78jYLuMGdVXXOAbhBL6L8gIAAcTrM5q/pnFsl6uyelqcBghMIVNenE6n0tPTlZ2dbXUUAGi1r3Yc0F5XreJjInTREMZ2AU4kZMrL9OnTtWnTJq1cudLqKADQakenA/h+BmO7ACcTMuUFAIJdtadBizaUSpKuYhJG4KQoLwAQID7YWKqaOq/6dInVqF5nWR0HCFiUFwAIEPNWN56oe+UoxnYBToXyAgABYK/rsL7Yvl+S9EPGdgFOifICAAFgwZo9MkbK6ZuotMRYq+MAAS1kyguXSgMIVsaYpquMOFEXOL2QKS9cKg0gWK3f7VJheZWiI+z63vDuVscBAl7IlBcACFZHT9SdODRF8TGRFqcBAh/lBQAsVO/16e9r90iSruSQEdAslBcAsNCSrftUUV2npE7ROn9AktVxgKBAeQEACx2dhPEHGamKcPAnGWgOflMAwCKuw/X6aHOZJA4ZAS1BeQEAi7y/fq/qGnwalNxJQ1PjrY4DBA3KCwBYZN6RQ0Y/HMl0AEBLhEx5YZA6AMGkpKJGK4oqZLNJU0amWh0HCCohU14YpA5AMFlY0LjXZUy/Luqe0MHiNEBwCZnyAgDBwhjzrUNGnKgLtBTlBQDa2dpdLu3YV62YSKYDAFqD8gIA7Wz+kUkYJ6anqFN0hMVpgOBDeQGAdlTv9enddXslST9kbBegVSgvANCOmA4AOHOUFwBoR/PWNB4yuiKT6QCA1uI3BwDaiaumXv/aXC6J6QCAM0F5AYB28t6R6QCGpMQpvTvTAQCtFTLlhRF2AQS6eUeuMrpyVA+mAwDOQMiUF0bYBRDIig9Ua1XxQdlt0hWZHDICzkTIlBcACGTzj4yoe+6AJCXHx1icBghulBcAaGPGGM1b3VherhrV0+I0QPCjvABAG8svPqidFTWKjXJo4tBkq+MAQY/yAgBt7G9H9rp8b1h3xUYxHQBwpigvANCGauu9em/dHknSVYztAvgF5QUA2tDHW8rlrm1QakKMzunXxeo4QEigvABAGzo6tssVI3vIbmdsF8AfKC8A0Eb2VXr0ydZ9kjhkBPgT5QUA2siCNbvl9RllpnXWgG5xVscBQgblBQDagDFG7+Q3HjK6ZjRjuwD+RHkBgDawfrdLW8sqFR1h1/dHpFodBwgpIVNemJgRQCA5utdl0tAUJXSItDgNEFpCprwwMSOAQOFp8GphQePYLldnccgI8LeQKS8AECj+talcrsP16p4Qo3MHJFkdBwg5lBcA8LN38kskSVeO6iEHY7sAfkd5AQA/KnfXasnXR8d24ZAR0BYoLwDgR/PW7JbPSFm9z1K/rp2sjgOEJMoLAPjJMWO7cKIu0GYoLwDgJwUlh1RYXqWYSLsuH9Hd6jhAyKK8AICfvLmi8UTdy4Z1V1wMY7sAbYXyAgB+UOVp0LvrGsd2uT6nl8VpgNBGeQEAP/h7wR7V1HnVr2tHZfc5y+o4QEijvACAH8xduVOSdH12mmw2xnYB2hLlBQDO0KY9bq3d5VKkw8bYLkA7oLwAwBl688hel4npKerSKdriNEDoo7wAwBk4XOfV/DW7JUnX56RZnAYID5QXADgD72/Yq8raBqUldtC5/ZmEEWgPIVNenE6n0tPTlZ2dbXUUAGHk6Ngu141Ok51JGIF2ETLlZfr06dq0aZNWrlxpdRQAYaKwvEorvqmQ3SZdM5pDRkB7CZnyAgDt7ejl0RcN6abk+BiL0wDhg/ICAK1QW+/V20cmYbw+mxF1gfZEeQGAVlhYsFuHaurV86wOGj+km9VxgLBCeQGAFjLG6K9fFkuSbhnTWw5O1AXaFeUFAFpoVfFBbdrrVkykXddyoi7Q7igvANBCc778RpI0JbOHOsdGWRsGCEOUFwBogVJXrT7YUCpJumVMH2vDAGGK8gIALfD68mI1+Ixy+iQqPTXe6jhAWKK8AEAzeRq8en1F49gu08b2sTYMEMYoLwDQTO+vL9X+qjqlxMdo4tBkq+MAYYvyAgDNdPRE3RtzeynSwZ9PwCr89gFAM6zZeVAFJYcU5bBrai4j6gJWorwAQDP86dPtkqTJGalK6hRtcRogvFFeAOA0tpVV6sNNZbLZpJ+O62d1HCDsUV4A4DT+tKRxr8vE9GQN6BZncRoAlBcAOIVdB2v094I9kqS7xw2wOA0AifICAKf04mdFavAZnTugizLSOlsdB4AoLwBwUgeqPHpzZeOgdD+9kL0uQKCgvADASbz0xTeqrfdpRM8EnTugi9VxABxBeQGAE6isrddfl30jSbp7XH/ZbDZrAwFoQnkBgBN4bflOVdY2qH/XjpqYnmJ1HADfEjLlxel0Kj09XdnZ2VZHARDk3LX1en7pDknSTy7sL7udvS5AIAmZ8jJ9+nRt2rRJK1eutDoKgCD33JLtqqiuU/+uHfXDkT2sjgPgO0KmvACAP5S6avXnz4skST+/dIgimIARCDj8VgLAtzz10deqrfdpdO+zdEl6stVxAJwA5QUAjthaWqm380skSTMvO5srjIAARXkBgCMeX7RFPiNdOjRFWb3PsjoOgJOgvACApGXbD+jjLeVy2G166NLBVscBcAqUFwBhzxij376/WZJ0Q04v9evayeJEAE6F8gIg7L2Tv0trd7nUMcqh/7h4oNVxAJwG5QVAWCuvrNWj/9gkSbr34oHqGhdtcSIAp0N5ARDWHl64Ue7aBg3rEa8fndfX6jgAmoHyAiBsvb9+r97fUKoIu02/uyqDAemAIMFvKoCw5Kqp138v3Cipcf6i9NR4ixMBaC7KC4Cw9Ov3Nml/lUf9u3bUPRcNsDoOgBagvAAIO59t26e383fJZpMev2qEYiIdVkcC0AKUFwBhpdxdq5+9vVaSNG1MH43uk2hxIgAtRXkBEDbqGnz66WurVeb2aGC3TnpwEiPpAsGI8gIgbPzy3Y3KLz6ouJgIPX/LaHWMjrA6EoBWoLwACAtvrNip15fvlM0m/d/Ukeqb1NHqSABaifICIOTlFx/UrIUbJEk/mzhY4wd3szgRgDNBeQEQ0nYdrNFPX81Xvdfoe8NSdPe4/lZHAnCGKC8AQtbOAzW67rmvVF7p0aDkTnrymgzZbDarYwE4Q5ytBiAkFe2v1tTnv1Kpu1b9kjrq5dtzOUEXCBH8JgMIOYXllbrhheUqr2y8JPq1O3PVLS7G6lgA/ITyAiCkbCl166YXl2t/VZ2GpMTp1R/lKqlTtNWxAPgR5QVAyJi3epd+sWCDauq8Gpoar1fvyNVZHaOsjgXAzygvAIJeTV2DZi3cqHfyd0mSzumXqOduGq2E2EiLkwFoC5QXAEFta2mlpr++WoXlVbLbpPsuHqR7Lhogh52rioBQRXkBEJQqa+v13JIdeuGzHfI0+NQtLlr/e/1IjenfxepoANoY5QVAUKn3+vTGip36339t04HqOknShYO66vfXZnBiLhAmKC8AgkJtvVf/XL9Xz3xcqB37qyVJ/ZI66uffG6KJ6ckMPgeEEcoLgID2dVml3lixU/NW75brcL0kKalTlO6bMEjXZ6cp0sFA4UC4obwACCjGGG3eW6klX+/TR5tKtXrnoaaf9ejcQVNz0nTruX3VidFygbAVsL/9NTU1Ovvss3XNNdfoySeftDoOgDbi9Rlt31el9btcWl50QEu+3qcyt6fp5w67TRcP6aapub10wcCuXEUEIHDLy29+8xudc845VscA4Cd1DT7tOlij4gM1Kj5QraL91dq4x62Ne9w6XO89ZtmYSLvG9k/ShYO66tJhKUqOZ2h/AP8WkOVl27Zt2rJliyZPnqwNGzZYHQfAt/h8RjX1XtXUNajG41VNnVdVnga5DtfLdbheh2rq5Dpcr32VHu2r9Ki80qPyylrtq/TIZ078mrFRDg1NjVdGz866YFBX5fRNVEyko30/GICg0eLysnTpUj3xxBPKz8/X3r17NX/+fE2ZMuWYZZxOp5544gmVlpYqIyNDTz/9tHJycpr9Hj/72c/0xBNP6Msvv2xpvDZTWF6l15YXWx0DAcKcZCN8+ued/onmW69vZE74XubIaxnTuOzR5Y4+16jxjs8YGUm+o/9ujLw+I6+v8fnepvtGDT4jn8+o3utTvffoP32qa/DJ03Tzqt7byg8vqUOkQ727xKpPl47q3SVWQ7rHaXiPBPVN6sThIADN1uLyUl1drYyMDN1+++268sorj/v53LlzlZeXp2effVa5ubmaPXu2Jk2apK1bt6pbt26SpMzMTDU0NBz33A8//FArV67UoEGDNGjQoIAqL7sPHdZLX3xjdQwgYNhsUmykQ7HREeoUHaH4DpFKaLpFqGunGHWNi1a3uGh1i49WSnzjfS5pBnCmbKY5/yt4sifbbMftecnNzVV2draeeeYZSZLP51NaWpruvfdezZgx47SvOXPmTL366qtyOByqqqpSfX29HnjgAc2aNeuEy3s8Hnk8/z65z+12Ky0tTS6XS/Hx8a39aMcp2l+td/JL/PZ6CH42tW4jfHTbfcpn2/796jbbid+r8fEj/zzyonab7ZjHG+/bZD/ymN1uk93WeHPYG58XYbfJYbcpwm6Xwy5FOuyKcNgV6bAp6si/x0TaFR3hUEykXVEOuzpGRyg6wk4RAeA3brdbCQkJzdp++7W81NXVKTY2Vu+8884xhWbatGk6dOiQFi5c2KLXnzNnjjZs2HDKq41++ctf6pFHHjnucX+XFwAA0HZaUl78OrrT/v375fV6lZycfMzjycnJKi0t9edbNZk5c6ZcLlfTraSEvSMAAISygLza6Khbb731tMtER0crOpr5TAAACBd+3fOSlJQkh8OhsrKyYx4vKytTSkqKP98KAACEKb+Wl6ioKGVlZWnx4sVNj/l8Pi1evFhjxozx51sBAIAw1eLDRlVVVSosLGy6X1RUpIKCAiUmJqpXr17Ky8vTtGnTNHr0aOXk5Gj27Nmqrq7Wbbfd5tfgAAAgPLW4vKxatUrjx49vup+Xlyep8YqiOXPm6LrrrtO+ffs0a9YslZaWKjMzU4sWLTruJF5/czqdcjqd8nq9p18YAAAErTO6VDoQteRSKwAAEBgsu1QaAACgrVFeAABAUKG8AACAoEJ5AQAAQSVkyovT6VR6erqys7OtjgIAANoQVxsBAADLtWT7HdBzG7XG0S7mdrstTgIAAJrr6Ha7OftUQq68VFZWSpLS0tIsTgIAAFqqsrJSCQkJp1wm5A4b+Xw+7dmzR3FxcbLZbH59bbfbrbS0NJWUlHBIqg2xntsH67l9sJ7bB+u5/bTVujbGqLKyUqmpqbLbT31KbsjtebHb7erZs2ebvkd8fDy/HO2A9dw+WM/tg/XcPljP7act1vXp9rgcFTJXGwEAgPBAeQEAAEGF8tIC0dHRevjhhxUdHW11lJDGem4frOf2wXpuH6zn9hMI6zrkTtgFAAChjT0vAAAgqFBeAABAUKG8AACAoEJ5AQAAQYXycgoVFRW68cYbFR8fr86dO+uOO+5QVVXVKZ8zbtw42Wy2Y24/+clP2ilx8HA6nerTp49iYmKUm5urFStWnHL5t99+W0OGDFFMTIyGDx+uf/7zn+2UNLi1ZD3PmTPnuO9uTExMO6YNTkuXLtXkyZOVmpoqm82mBQsWnPY5n376qUaNGqXo6GgNGDBAc+bMafOcwa6l6/nTTz897vtss9lUWlraPoGD1GOPPabs7GzFxcWpW7dumjJlirZu3Xra57X332jKyynceOON2rhxoz766CP94x//0NKlS3XXXXed9nl33nmn9u7d23T73e9+1w5pg8fcuXOVl5enhx9+WKtXr1ZGRoYmTZqk8vLyEy7/5ZdfaurUqbrjjju0Zs0aTZkyRVOmTNGGDRvaOXlwael6lhpHzPz2d7e4uLgdEwen6upqZWRkyOl0Nmv5oqIiXX755Ro/frwKCgp0//3360c/+pE++OCDNk4a3Fq6no/aunXrMd/pbt26tVHC0LBkyRJNnz5dX331lT766CPV19dr4sSJqq6uPulzLPkbbXBCmzZtMpLMypUrmx57//33jc1mM7t37z7p8y688EJz3333tUPC4JWTk2OmT5/edN/r9ZrU1FTz2GOPnXD5a6+91lx++eXHPJabm2t+/OMft2nOYNfS9fzSSy+ZhISEdkoXmiSZ+fPnn3KZhx56yAwdOvSYx6677jozadKkNkwWWpqznj/55BMjyRw8eLBdMoWq8vJyI8ksWbLkpMtY8TeaPS8nsWzZMnXu3FmjR49uemzChAmy2+1avnz5KZ/72muvKSkpScOGDdPMmTNVU1PT1nGDRl1dnfLz8zVhwoSmx+x2uyZMmKBly5ad8DnLli07ZnlJmjRp0kmXR+vWsyRVVVWpd+/eSktL0xVXXKGNGze2R9ywwve5fWVmZqp79+665JJL9MUXX1gdJ+i4XC5JUmJi4kmXseI7HXITM/pLaWnpcbsXIyIilJiYeMpjpjfccIN69+6t1NRUrVu3Tj//+c+1detWzZs3r60jB4X9+/fL6/UqOTn5mMeTk5O1ZcuWEz6ntLT0hMtz7PrkWrOeBw8erL/85S8aMWKEXC6XnnzySY0dO1YbN25s88lOw8nJvs9ut1uHDx9Whw4dLEoWWrp3765nn31Wo0ePlsfj0Ysvvqhx48Zp+fLlGjVqlNXxgoLP59P999+vc889V8OGDTvpclb8jQ678jJjxgw9/vjjp1xm8+bNrX79b58TM3z4cHXv3l0XX3yxtm/frv79+7f6dYG2NmbMGI0ZM6bp/tixY3X22Wfrueee06OPPmphMqDlBg8erMGDBzfdHzt2rLZv366nnnpKr7zyioXJgsf06dO1YcMGff7551ZHOU7YlZcHHnhAt9566ymX6devn1JSUo47sbGhoUEVFRVKSUlp9vvl5uZKkgoLCykvkpKSkuRwOFRWVnbM42VlZSddrykpKS1aHq1bz98VGRmpkSNHqrCwsC0ihq2TfZ/j4+PZ69LGcnJyAnJDHIjuueeepgtVTrfn1Yq/0WF3zkvXrl01ZMiQU96ioqI0ZswYHTp0SPn5+U3P/fjjj+Xz+ZoKSXMUFBRIatyFCSkqKkpZWVlavHhx02M+n0+LFy8+5v/6v23MmDHHLC9JH3300UmXR+vW83d5vV6tX7+e766f8X22TkFBAd/n0zDG6J577tH8+fP18ccfq2/fvqd9jiXf6TY7FTgEXHrppWbkyJFm+fLl5vPPPzcDBw40U6dObfr5rl27zODBg83y5cuNMcYUFhaaX/3qV2bVqlWmqKjILFy40PTr189ccMEFVn2EgPTmm2+a6OhoM2fOHLNp0yZz1113mc6dO5vS0lJjjDE333yzmTFjRtPyX3zxhYmIiDBPPvmk2bx5s3n44YdNZGSkWb9+vVUfISi0dD0/8sgj5oMPPjDbt283+fn55vrrrzcxMTFm48aNVn2EoFBZWWnWrFlj1qxZYySZP/zhD2bNmjWmuLjYGGPMjBkzzM0339y0/I4dO0xsbKx58MEHzebNm43T6TQOh8MsWrTIqo8QFFq6np966imzYMECs23bNrN+/Xpz3333Gbvdbv71r39Z9RGCwk9/+lOTkJBgPv30U7N3796mW01NTdMygfA3mvJyCgcOHDBTp041nTp1MvHx8ea2224zlZWVTT8vKioykswnn3xijDFm586d5oILLjCJiYkmOjraDBgwwDz44IPG5XJZ9AkC19NPP2169eploqKiTE5Ojvnqq6+afnbhhReaadOmHbP8W2+9ZQYNGmSioqLM0KFDzXvvvdfOiYNTS9bz/fff37RscnKyueyyy8zq1astSB1cjl6S+93b0XU7bdo0c+GFFx73nMzMTBMVFWX69etnXnrppXbPHWxaup4ff/xx079/fxMTE2MSExPNuHHjzMcff2xN+CByonUs6ZjvaCD8jbYdCQsAABAUwu6cFwAAENwoLwAAIKhQXgAAQFChvAAAgKBCeQEAAEGF8gIAAIIK5QUAAAQVygsAAAgqlBcAABBUKC8AACCoUF4AAEBQobwAAICg8v8BgRp6Ju5lR0gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df)\n",
    "from matplotlib import pyplot as plt \n",
    "plt.plot(df['GateV'],abs(df['DrainI']),label=f'{tp}SourceI')\n",
    "plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Vin           Iin  Vdd           Idd  VGND          IGND  Iout     Vout\n",
      "0    0.00  1.565000e-09  2.0  4.623000e-09   0.0  6.283000e-09   0.0  2.00012\n",
      "1    0.02 -7.927000e-09  2.0  1.675400e-08   0.0  5.921000e-09   0.0  2.00008\n",
      "2    0.04 -5.472000e-09  2.0  1.528900e-08   0.0 -2.040000e-10   0.0  2.00004\n",
      "3    0.06  2.283000e-09  2.0  9.957000e-09   0.0 -7.473000e-09   0.0  2.00008\n",
      "4    0.08  1.990000e-09  2.0 -8.324000e-09   0.0  6.541000e-09   0.0  2.00008\n",
      "..    ...           ...  ...           ...   ...           ...   ...      ...\n",
      "96   1.92  1.110100e-09  2.0 -4.850000e-09   0.0  1.212800e-08   0.0  0.00004\n",
      "97   1.94 -6.031400e-09  2.0  9.402000e-09   0.0 -1.007100e-08   0.0  0.00002\n",
      "98   1.96 -3.475700e-09  2.0 -6.011000e-09   0.0  1.324800e-08   0.0  0.00002\n",
      "99   1.98 -6.848900e-09  2.0  7.620000e-09   0.0 -1.625500e-08   0.0  0.00004\n",
      "100  2.00 -2.925000e-10  2.0 -6.336000e-09   0.0  1.370100e-08   0.0  0.00003\n",
      "\n",
      "[101 rows x 8 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas\n",
    "df = pandas.DataFrame(res[1:],columns=['Vin','Iin','Vdd','Idd','VGND','IGND',\"Iout\",'Vout'],dtype=float)\n",
    "\n",
    "from matplotlib import pyplot as plt \n",
    "plt.plot(df['Vin'],df['Vout'])\n",
    "plt.show()\n",
    "\n",
    "from datetime import datetime\n",
    "tStamp = datetime.strftime(datetime.now(),\"%Y%m%d_%H%M%S\")\n",
    "df.to_excel(f\"./result/inverter_{tStamp}.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IVCURVE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#b1500a smu\n",
    "from starInstrument import *\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "n=1\n",
    "\n",
    "df = IVCurve()\n",
    "plt.plot(df['Voltage'],df['Current'])\n",
    "\n",
    "from datetime import datetime\n",
    "tStamp = datetime.strftime(datetime.now(),\"%Y%m%d_%H%M%S\")\n",
    "df.to_excel(f\"./result/R4-1500SP-{tStamp}.xlsx\",sheet_name='Data')\n",
    "\n",
    "plt.show()\n",
    "closeIns()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
